- Task 1 - Video mode Localisation
- Task 2 - Video Mode End-to-End
- Task 3 - Still image mode Localisation
- Task 4 - Still image mode Word Recognition
- Task 5 - Still image mode End-to-End
Inactive evaluations
method: Clova AI / Lens2018-10-05
Authors: Seolki Baek, Geonmo Gu, Jeongo Seo
Description: Description: Our model is featured by CNN/RNN-based encoder and Hybrid CTC/Attention decoder. Moreover we proposed new text synthesis tools to make our model robust and high performance in the wild.
method: DictNet2017-09-02
Authors: RRC DOST organizers
Description: Synthetic Data and Artificial Neural Networks for Natural Scene Text Recognition
M Jaderberg, K Simonyan, A Vedaldi, A Zisserman
NIPS Deep Learning Workshop 2014
method: tesseract 4.00 (LSTM)2017-08-31
Authors: RRC DOST organizers
Description: We've just passed the images through the Tesseract OCR 4.00 with the new Neural Nets (LSTM) engine mode.
Description Paper Source Code
| Date | Method | Total Edit distance (case sensitive) | Correctly Recognised Words (case sensitive) | T.E.D. (case insensitive) | C.R.W. (case insensitive) | |||
|---|---|---|---|---|---|---|---|---|
| 2018-10-05 | Clova AI / Lens | 109.1976 | 65.15% | 108.0071 | 65.36% | |||
| 2017-09-02 | DictNet | 501.4407 | 4.74% | 477.9318 | 8.45% | |||
| 2017-08-31 | tesseract 4.00 (LSTM) | 465.1497 | 7.22% | 459.1036 | 7.42% |